“When digital transformation is done right, it’s like a caterpillar turning into a butterfly. But when done wrong, all you have is a really fast caterpillar.”
This quote by George Westerman reveals the meaning of data transformation. Moving data faster isn’t enough, it is necessary to reshape how it is structured and interpreted.
Think about the data in your business. It’s everywhere: customer details, sales numbers, website activity, inventory records. Unfortunately, raw data isn’t always handy. It can be messy, incomplete, or stored in different formats. As a result, it doesn’t work well together. This can slow down your operations and create unnecessary headaches.
That’s why data transformation techniques are so vital. It helps clean, organize, and convert your data into a relevant format. With the right approach, you can optimize your processes and get insights that will lead you to better business decisions.
In this article, Forbytes experts will share key methods to help you transform your data and turn it into a valuable asset for your company. Let’s dive in!
What Is Data Transformation?
Imagine: you have a messy pile of papers like receipts, invoices, and notes. They are scattered around your desk. You know there’s valuable information there. But finding what you need is difficult. That’s exactly what raw data is like for your businesses. It is disorganized and hard to use.
Data analytics transformation is the process of changing the structure, format, or type of data. As a result, it will become available and usable. For instance, a company may gather raw data from multiple sources, process it through a data transformation technique like normalization, and store the refined data in a central repository for easy access and analysis.
In fact, data transformation is like sorting various papers, putting them in folders, and creating a system. So, you can quickly find the proper information when you need it.
Need help with data transformation? Our data engineering experts can help you rethink your big data strategy and turn insights into action.
Types of Data Transformation
Data transformation can be constructive, destructive, formatting, or structural. Each one has its purpose and helps prepare data for analysis or reporting.
Think of these data analytics transformations as organizing your room. Constructive changes are like adding new shelves to create more storage space. Destructive changes are about clearing out what no longer serves you, like tossing old clothes. Formatting is when you neatly arrange everything, making it more visually appealing and easier to use. And structural changes are more crucial, like moving furniture around to improve the room’s flow.
Let’s focus on each type of data transformation and discuss it in a business context.
Constructive transformation: adding new info
This type of data transformation adds new things to your data. It helps you get more value by creating new columns or features.
Let’s say you have a column with a customer’s date of birth. You can create a new column that shows their age. Or, if you have daily sales, you can add up the totals for the week.
Why to use constructive data analytics transformation? It can give your data more meaning and make analysis or machine learning smarter.
Destructive transformations: cleaning and removing
Destructive data transformation removes things you don’t need. Think of it as cleaning up your data.
For example, you delete duplicate records, fix typos, or remove empty values. If you have a column that doesn’t help with your goals, you delete it.
We recommend using destructive data analytics transformation to get rid of noise and focus only on the important stuff. It makes your results more accurate.
Formatting transformations: making data look right
This type of transformation changes how your data is displayed or organized. For example, dates might be converted to the same format (like DD/MM/YYYY), or names might be sorted alphabetically.
Why use it? It makes your data easier to read, share, or visualize. It doesn’t alter the data itself, just how it’s presented.
Structural transformations: changing data shape
Here, you’re changing the structure of your data to make it more usable. For example, you might split one table into several smaller ones (normalization), combine different datasets into one, or reshape data from rows to columns for analysis.
This type of data analytics transformation is useful because it prepares your data for machine learning, reporting, or combining information from different systems.
Want to get the most out of your data and turn it into real value for your business? Contact Forbytes, and we’ll get back to you with tailored solutions that fit your needs.
Top 6 Data Transformation Techniques
You can use many ways to transform data, and each method solves a specific problem. Think of them like tools in a toolbox, each one helps you fix or improve your data in different ways. Let’s look at a few common ones and why they matter.
Data cleaning
Data can be messy, full of mistakes, missing info, or repeated entries. Data cleaning, also called data manipulation, fixes that. You can use this data transformation technique almost every time. You may want clean data before doing any serious analysis.
How it works:
- Removes duplicates
- Fills in missing data
- Fixes typos
- Makes sure everything follows the same format
For example, If “New York” is written as “NY” and “newyork” in the same column, you make them all say “New York”.
Data normalization
Some numbers are huge (like income), and some are small (like star ratings). This can confuse data models. So, it is recommended to scale or normalize data. This data transformation technique works perfectly when you use machine learning or compare numbers with very different scales.
How it works:
- Shrinks numbers to a range like 0 to 1
- Helps make all values fair and balanced
For example, if one column goes from 1 to 10,000 and another goes from 0 to 5, normalization puts them on the same scale.
Feature engineering
Sometimes the data you have isn’t enough. You need to create more helpful data. For this, you can use feature engineering or attribute construction. This data transformation technique is useful when you want to improve ML models or find better insights.
How it works:
- Adds new columns by calculating values
- Combines features
- Uses math like log or square root
- Extracts info (like getting the day from a date)
For instance, you take a full date (e.g., “2025-04-03”) and create a new column called “Day of the Week.”
Data generalization
Too much detail can make it hard to spot patterns. With data generalization, this problem is solved as this technique simplifies data. It is helpful when data is very detailed or complex, like images, sounds, or large datasets.
How it works:
- Groups small details into bigger ideas
- Summarizes or clusters similar things
For example, instead of showing every age, you can group people into age ranges like “20-29”, “30–39”, etc.
Data discretization
Your data can be made up of continuous numbers like age, temperature, or income. Discretization takes those numbers and turns them into easy-to-understand groups or categories. You can use this data transformation technique to simplify data for analysis or use tools that prefer categories over raw numbers like decision trees or rule-based models.
How it works:
- Breaks data into chunks (like age groups: 0–18, 19–35, etc.)
- Uses methods like equal width, equal frequency, or clustering
- Makes continuous data easier to understand and analyze
For example, instead of dealing with hundreds of different ages, you could group them into “young,” “adult,” and “senior.” This makes it easier to see trends across age groups.
Data aggregation
Raw data can be overwhelming, especially when it’s too detailed. Data aggregation simplifies things by summarizing the data so it’s easier to understand. It’s great for creating financial reports, sales forecasts, or performance summaries.
How it works:
- Combines data using averages, sums, or counts
- Groups it by time, location, or category
- Helps highlight broader patterns
For example, instead of tracking every single sale, you can group them by month to see how business is doing over time.
How You Can Benefit From Data Transformation
Just having tons of data isn’t enough. If your data is messy or scattered, it’s hard to get anything useful out of it. That’s why data analytics transformation is the process that gets your data cleaned up, organized, and ready to do something with it.
But what else can data transformation bring to your table?
First, when you modify your data, you can be sure that you achieve better data quality. During data transformation, a system checks and fixes errors and removes duplicates. This makes your data more accurate and reliable.
Second, when your data is structured the same way across your systems, it’s easier for everyone to understand and use. No more confusion over different formats or mismatched numbers, with data analytics transformation, you’ll get more consistent data.
Clean, transformed data is more usable. You can run reports, build dashboards, and make data-driven decisions with confidence. It’s all about unlocking the full potential of the information you already have. So, data transformation is a way to smarter use of data.
Once your data is in a standardized format and stored in one place (especially if it’s cloud-based), it’s super easy to find and use. No more hunting through different systems or spreadsheets.
See just how valuable data transformation can be for your company. When you change how you handle and process data, it can completely transform the way you operate in the market.
What Challenges Might You Face with Data Transformation?
Data analytics transformation offers a lot of great benefits. But let’s be honest, it also comes with its fair share of hurdles.
Today, companies gather data from many different sources: apps, websites, tools, and more, which makes it tricky to organize and understand.
Turning that messy data into something useful requires more than just a good tool. You may need people who understand your data, can spot issues, and know how to fix them. Without that expertise, things can go wrong quickly and lead to poor decisions.
It’s also important to know that data transformation takes time and effort. It can slow things down if your systems are already overloaded, and it often requires a fair amount of manual work.
On top of that, there are costs involved: paying for the right tools, hiring skilled experts, and keeping everything running smoothly.
For smaller businesses, that can feel like a lot. But if you take your time, build a smart strategy, and follow best practices, the rewards are definitely worth it.
And if you ever need a hand, we’re here to help. With our expertise in data engineering, you’ll save time, avoid costly mistakes, and get real value from your data.
How Data Transformation Works
Now when you are aware of the benefits and challenges of data analytics transformation, it’s high time to see how this process works. Here’s how it usually goes:
- Step 1: Data discovery: You should start by figuring out where your data is coming from and what shape it’s in. If you’re using tools like ELT, the raw data gets pulled into your data warehouse first.
- Step 2: Data mapping: Now it’s time to explore the data. You check what’s there, what’s missing, and how things connect. This sets the plan for what needs to change. It can be a lot, but intelligent automation tools make it easier.
- Step 3: Data transformation: At this stage, you take the messy data and turn it into a format that works for your business.
- Step 4: Review: Before sharing anything, you test the data. You make sure the numbers look right, the structure makes sense, and everything fits together.
- Step 5: Documentation: Once your data is good to go, you document everything: what was changed, why, and how to use it. This helps your team understand the data and trust the insights it provides.
These simple steps can assist you in transforming your data and turning them from raw datasets into structured data that can be used for your business insights.
How Forbytes Helped a Logistics Company Transform Its Data
One of our recent clients is a leading European logistics company. They have been automating their warehouses for years. But as they expanded, their data became scattered across multiple systems.
So, it was difficult to track efficiency, manage invoices, and make real-time decisions. Our client needed a smarter way to use their data. So, they turned to Forbytes to solve this issue.
Their data was from ERPs, warehouse sensors, and finance tools. It was siloed, inconsistent, and full of duplicates. That’s why reliable reporting was nearly impossible.
To fix that, we designed a modern data platform using Azure Data Lake Gen2 and Databricks. Here’s how we transformed their data into a strategic asset:
- Data ingestion and consolidation: We pulled data from all their systems into one centralized storage layer.
- Cleaning and validation: We removed duplicates, standardized formats, and fixed inconsistencies.
- Layered data architecture: Using Databricks, we organized data into bronze (raw), silver (cleaned), and gold (business-ready) layers.
Once the system was ready, we used Power BI to build real-time dashboards and make their operations and finances more visible. As a result, reports that used to take days were now generated in seconds. Plus, manual work dropped significantly.
Need help with data transformation? Reach out to our team, we’ll make sure your data starts working for you.
Let Forbytes Transform Your Data Effectively and With Zero Downtime
Data transformation techniques are essential for extracting insights and making informed decisions. That’s why it’s crucial to rethink your data management approach, especially if growth is your goal.
As you can see, data transformation can be both rewarding and challenging. It all comes down to how you approach your data and who you trust to help implement your big data strategy.
Need a team to assist? Forbytes has you covered. Our data engineering services will help you structure, manage, and optimize your data to streamline operations and drive business efficiency.
